Bridging Between Computer and Robot Vision Through Data Augmentation: A Case Study on Object Recognition

Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collection of images of object categories downloaded from the Web. This kind of images is very different from the situated and embodied visual experience of robots deployed in unconstrained settings. To reduce the gap between these two visual experiences, this paper proposes a simple yet effective data augmentation layer that zooms on the object of interest and simulates the object detection outcome of a robot vision system. The layer, that can be used with any convolutional deep architecture, brings to an increase in object recognition performance of up to 7{\%}, in experiments performed over three different benchmark databases. An implementation of our robot data augmentation layer has been made publicly available

Bibliometric Maps of BIM and BIM in Universities: A Comparative Analysis

Building Information Modeling (BIM) is increasingly important in the architecture and engineering fields, and especially in the field of sustainability through the study of energy. This study performs a bibliometric study analysis of BIM publications based on the Scopus database during the whole period from 2003 to 2018. The aim was to establish a comparison of bibliometric maps of the building information model and BIM in universities. The analyzed data included 4307 records produced by a total of 10,636 distinct authors from 314 institutions. Engineering and computer science were found to be the main scientific fields involved in BIM research. Architectural design are the central theme keywords, followed by information theory and construction industry. The final stage of the study focuses on the detection of clusters in which global research in this field is grouped. The main clusters found were those related to the BIM cycle, including construction management, documentation and analysis, architecture and design, construction/fabrication, and operation and maintenance (related to energy or sustainability). However, the clusters of the last phases such as demolition and renovation are not present, which indicates that this field suntil needs to be further developed and researched. With regard to the evolution of research, it has been observed how information technologies have been integrated over the entire spectrum of internet of things (IoT). A final key factor in the implementation of the BIM is its inclusion in the curriculum of technical careers related to areas of construction such as civil engineering or architecture

Does the Tax Code Favor Robots?

In recent months, a number of scholars and commentators have articulated versions of the following argument: (1) U.S. tax law favors capital over labor;1 (2) Robots are capital; 2 (3) Therefore, U.S. tax law favors robots over labor. 3 Three implications tend to be drawn from this syllogism: (a) that U.S. tax law leads to inefficient investments in automation;4 (b) that automation—because it is capital-intensive and capital is tax-favored—will result in a reduction in tax revenues;5 and (c) that policymakers should respond to the automation trend either by imposing explicit taxes on robots or by raising taxes on all capital.6 This short essay seeks to illustrate why the line of argument above is misguided. First, the claim that U.S. tax law is biased toward capital rests entirely on an unstated (and uncertain) normative premise: that the United States should tax income rather than consumption. If an income tax is the baseline, then U.S. tax law exhibits a pro-capital bias; if a consumption tax is the baseline, then U.S. tax law exhibits an anti-capital bias. Which baseline we choose depends on normative choices that claims of capital-favoritism tend to occlude. Second, robots do not only (or even primarily) represent “capital”; they also embed the labor of engineers and others. The labor of robot makers is often taxed at unfavorable rates relative to the labor of the workers whom automation threatens to displace. Third, the idea that U.S. tax law incentivizes firms to replace human workers with robots rests on doubtful logic, and the claim that automation will erode the tax base finds little support either. This essay is not an argument against capital income taxation or a defense of the current Code, which does tax capital income but not all that much. I believe, though, that the case for capital income taxation will be stronger if it is based on firm foundations rather than on dubious claims of robot favoritism. The essay also is not a full treatment of the arguments for and against taxing capital. Its objective is to evaluate one such argument and to show why it is unpersuasive. Part I of the essay examines the claim that the U.S. tax system favors capital over labor. Part II turns to the question of whether robots represent capital or embedded labor. Part III considers the case for explicit taxation of robots or broader taxation of capital once illusions about the tax code’s pro-robot bias are cleared away

Electronics and control technology

Until recently, there was no requirement to learn electronics and control technology in the New Zealand school curriculum. Apart from isolated pockets of teaching based on the enthusiasm of individual teachers, there is very little direct learning of electronics in New Zealand primary or secondary schools. The learning of electronics is located in tertiary vocational training programmes. Thus, few school students learn about electronics and few school teachers have experience in teaching it. Lack of experience with electronics (other than using its products) has contributed to a commonly held view of electronics as out of the control and intellectual grasp of the average person; the domain of the engineer, programmer and enthusiast with his or her special aptitude. This need not be true, but teachers' and parents' lack of experience with electronics is in danger of denying young learners access to the mainstream of modern technology